Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi

Anopheles stephensi, an invasive malaria vector native to South Asia and the Arabian Peninsula, was detected in Djibouti’s seaport, followed by Ethiopia, Sudan, Somalia, and Nigeria. If An. stephensi introduction is facilitated through seatrade, similar to other invasive mosquitoes, the identification of at-risk countries are needed to increase surveillance and response efforts. Bilateral maritime trade data is used to (1) identify coastal African countries which were highly connected to select An. stephensi endemic countries, (2) develop a prioritization list of countries based on the likelihood of An. stephensi introduction through maritime trade index (LASIMTI), and (3) use network analysis of intracontinental maritime trade to determine likely introduction pathways. Sudan and Djibouti were ranked as the top two countries with LASIMTI in 2011, which were the first two coastal African countries where An. stephensi was detected. With Djibouti and Sudan included as source populations, 2020 data identify Egypt, Kenya, Mauritius, Tanzania, and Morocco as the top countries with LASIMTI. Network analysis highlight South Africa, Mauritius, Ghana, and Togo. These tools can prioritize efforts for An. stephensi surveillance and control in Africa. Surveillance in seaports of identified countries may limit further expansion of An. stephensi by serving as an early warning system.

www.nature.com/scientificreports/ Unlike An. arabiensis, An. stephensi, is a unique malaria vector because of its ability to thrive in artificial containers in urban environments. This species is found across South and South-East Asia and the Arabian Peninsula, where it is a primary malaria vector and responsible for both urban and rural malaria transmission. Most malaria vector control efforts are focused on rural habitats, and the ability for malaria vectors to thrive in urban environments may threaten progress made on malaria control and elimination.
In 2012, An. stephensi was first detected on the African continent in a livestock quarantine station in a seaport in Djibouti 9 . By 2016, it was then detected in neighboring Ethiopia 10 . By 2018 11 or 2019 12 , An. stephensi was detected near seaports in Sudan and Somalia 13 . It was also most recently detected in Nigeria in 2020 14 . With An. stephensi having unique ecological characteristics, and the first detection of the species in seaports, it has been hypothesized that An. stephensi introduction was likely facilitated through maritime trade. Further supporting the similarities between An. stephensi and Ae. aegypti may be the fact that in Ethiopia, a large percentage (40% 15 or greater 16 ) of the habitats where An. stephensi larvae were detected, Ae. aegypti was also detected. With invasive An. stephensi populations now established in these countries, there is a new threat to malaria control on the African continent. Population genetic analyses suggest the potential source of introduction is South Asia 17 .
The invasion of this malaria vector has the potential to significantly impact global malaria control and elimination efforts 18 . For example, in Djibouti, An. stephensi has been linked to malaria outbreaks in 2013 9 and since initial detection in Djibouti, malaria cases have increased 30-fold 19 . Additionally, although it shows a seasonal variability in abundance in Asia, it has been detected year-round through the hot, dry season in Africa 20 . Recent laboratory studies on invasive Djiboutian and Ethiopian An. stephensi specimens reveal that as in Asia, these populations are competent vectors for both Plasmodium vivax and Plasmodium falciparum 20 . Thus, countries may need to expand their malaria testing protocol. Further, field data have shown confirmation of P. vivax sporozoites in An. stephensi in Ethiopia 21 , and high levels of resistance to nearly all insecticides used in malaria vector control.
Urban centers of sub-Saharan Africa tend to have lower malaria transmission rates than rural areas. However, urbanization in these areas increase breeding habitats and primary vector diversity which will lead to higher risk of transmission 22,23 . A recent habitat suitability modeling study predicted that the further invasion of An. stephensi into urban locations on the African continent could put an additional 126 million people at risk of malaria 24 .
To address this global challenge and proactively mitigate the threat of An. stephensi, prioritization activities are necessary to identify where this invasive mosquito is likely to be introduced, particularly if movement is facilitated by the movement of cargo through marine shipping. To better understand the invasion of An. stephensi, we describe: (1) United Nations Conference on Trade and Development (UNCTAD) maritime trade data from 2011, prior to the detection of An. stephensi in Djibouti, and habitat suitability to determine whether historical connectivity identify Djibouti and Sudan as high risk countries for An. stephensi introduction, (2) a prioritization list of coastal African countries for immediate surveillance based on 2020 data to allow for early detection, rapid response, and limit further introduction of the vector in Africa, and (3) an interactive network model of intracontinental transport routes in Africa allowing for future prioritization hierarchies for surveillance if/when An. stephensi is detected in new locations.

Materials and methods
Days at sea, habitat suitability index, trade index. Due to the initial detection of An. stephensi in the port city of Djibouti City, maritime trade data were examined. We ranked the maritime trade connection between countries with known An. stephensi populations (India, Pakistan, Saudi Arabia, and United Arab Emirates) and coastal African countries. Other countries with An. stephensi populations such as Iraq, Iran, and Thailand, exhibited lower trade levels and were not included.
We used UNCTAD's Liner Shipping Bilateral Connectivity Index (LSBCI), an index created from trade data from MDS Transmodal (https:// www. mdst. co. uk), to measure the amount of connectivity between each pair of countries. The LSBCI factors in five maritime trade indicators. The first is the number of transshipments, when goods are unloaded and moved to another vessel, to get from country j to country k. Secondly, LSBCI factors in the number of countries which have direct routes to both countries in the pair (e.g., four countries have direct connections to both country j and country k). The third indicator is the number of common connections with one transshipment shared between the countries. The level of competition on services that connect the countries, measured by the number of carriers operating on this route, serves as another indicator. Finally, the size of the largest ship on the route with the fewest carriers is considered in calculating LSBCI for a country pair, which can serve as a metric of capacity on sea routes. Each indicator is normalized by subtracting the minimum value from the raw value and dividing by the range. LSBCI is the simple average of the normalized value of these five indicators 25 . The inability to examine specific ports of call or transshipments is a limitation of this dataset. Future examinations of trade and An. stephensi may benefit from paid datasets from maritime trade operators.
We took the LSBCI value and divided it by the number of days required to travel by shipping vessel between the closest and largest ports of the countries. This was calculated via Searoutes which uses the automatic identification system (AIS) of vessels to track them and calculate average time between ports 26 . The same vessel speed was used in this calculation to maintain uniformity in measuring distance. This compiled index which includes (1) maritime trade degree of connectivity and (2) time between ports (in days) will be referred to as the likelihood of An. stephensi introduction through maritime trade index (LASIMTI).
Additionally, we incorporated Sinka et al. 's Habitat Suitability Index (HSI), which uses, in order of importance, annual mean temperature, population density, seasonal precipitation, surface wetness, vegetation, and other environmental factors to evaluate locations with suitable environments for An. stephensi habitation 24 . Using R (https:// www.r-proje ct. org/), a data set of countries was ranked by LASIMTI as well as both LASIMTI and the HSI. Ethiopia is landlocked and therefore was not included in this study. Finally, maritime trade data from 2020 was evaluated to assess further spread along this pathway. Potentially important to note, the UNCTAD estimates that maritime trade fell by 4.1% in 2020 due to the COVID-19 pandemic. However, they also predict a rebound of 4.8% in 2021 27 .
Maritime trade data from 2020 was used to create a network model of intracontinental African trade between coastal African countries. The connectivity of coastal African nations was examined based on country pairs' LSBCI. The top three countries, as ranked by LSBCI for each country, were highlighted as links between the nodes. In cases of ties, both countries were included (e.g. Sudan has four country pairs because Egypt, Kenya, and Morocco had the same LSBCI). Another network model was created with a cutoff of 14 days of travel between each node as historical reports show that An. stephensi eggs can resist desiccation in soil for up to 14 days 28 (Supplemental Fig. 1). Edges are weighted by the LSBCI value and nodes are weighted by the number of connected countries. Djibouti and Sudan are differentiated due to their established An. stephensi populations. This network model was created with r in RStudio utilizing the igraph and visNetwork packages.
Network centrality is often calculated with eigenvector centrality, which measures the influence of nodes by factoring in the number of connections and the number of connections of its neighbors. PageRank is a variant of eigenvector centrality which considers the direction of edges making it useful for understanding trade 29 . PageRank was used for this network model because of the directed, weighted edges. This rank value determines the centrality of a single node in a network based upon how many connections point towards and away from the node as well as each of its neighbors' total number of connections. Edge weights and values of other nodes are factored in as well. The PageRank value ultimately is a probability distribution of the nodes in the network. In this network this would essentially be if a single vessel was selected, the probability that it would be found at a given node. PageRank was calculated in RStudio with the igraph package.
Ethics approval and consent to participate. Not applicable.

Maritime index in 2011 prior to detection of An. stephensi in Africa identifies Sudan and Djibouti as highest for risk of introduction. 2011 Maritime trade data from UNCTAD point to Sudan and
Djibouti as the top two connected countries to the source populations (India, Pakistan, Saudi Arabia, and UAE) when the LASIMTI is summed. The next three countries are Egypt, Kenya, and Tanzania (Table 1, full table: Supplemental Table 1). When HSI is included the top five remain the same (Supplemental Table 2).

Maritime index in 2016 following detection of An. stephensi in Djibouti and Ethiopia highlights Sudan at highest for risk of introduction. 2016 UNCTAD maritime trade data shown in Sup-
plemental Table 3 highlight, in order, Sudan, Djibouti, Egypt, Mauritius, and Kenya when ranked by the sum of LASIMTI to the source populations. When this data is ranked first by HSI, the top 5 countries are Sudan, Djibouti, Egypt, Kenya, and Tanzania (Supplemental Table 4).
Anopheles stephensi was established in Djibouti in 2012 so after this date, Djibouti can be included as a source population which gives the top five countries as Sudan, Egypt, Mauritius, Kenya, and Tanzania when ranked by their sum of LASIMTI to each source population (Supplemental Table 5). The top five countries when ranked by HSI and then LASIMTI sum are Sudan, Egypt, Kenya, Tanzania, and Morocco when Djibouti is included as a source population (Supplemental Table 6).  www.nature.com/scientificreports/ when ranked by the sum of LASIMTI (Fig. 1, Supplemental Table 7). Sudan and Djibouti remain the top two connected countries for all the three years examined (Fig. 2). The data utilizing both the HSI and LASIMTI place Sudan, Djibouti, Egypt, Kenya, and Tanzania as the top five countries (Supplemental Table 8).

Maritime index in
Since An. stephensi populations have been confirmed in Sudan as well in 2019, these data were further examined with Djibouti and Sudan included as potential source populations for An. stephensi. With Djibouti and Sudan included as source populations, the top five countries at risk of An. stephensi introduction are Egypt, Kenya, Mauritius, Tanzania, and Morocco (Table 2). When the HSI is also included in the ordering, the top five countries are Egypt, Kenya, Tanzania, Morocco, and Libya (Table 3). Full tables can be found in the supplement (Supplemental Tables 9 and 10, respectively). Intracontinental connectivity network model. The interactive network model reveals degrees of connectivity within coastal nations on the African continent (Fig. 3). Specifically, it highlights highly connected   Table 11). Djibouti and Sudan are ranked 7th (0.030) and 32nd (0.0045) respectively. Egypt was highlighted often as being at risk of introduction by the LASIMTI ranking. In the PageRank centrality analysis, Egypt is ranked 6th with a rank value of 0.0353. Other countries that were highlighted are Kenya (11th, 0.0164) and Tanzania (12th, 0.0156).

Discussion
With human movement and globalization, invasive container breeding vectors responsible for dengue, Zika, chikungunya and now malaria, with An. stephensi, are being introduced and establishing populations in new locations. They are bringing with them the threat of increasing or novel cases of vector-borne diseases to new locations where health systems may not be prepared.
Anopheles stephensi was first detected on the African continent in Djibouti in 2012 and has since been confirmed in Ethiopia, Somalia, and Sudan. Unlike most malaria vectors, An. stephensi is often found in artificial containers and in urban settings. This unique ecology combined with its initial detection in seaports in Djibouti, Somalia, and Sudan has led scientists to believe that the movement of this vector is likely facilitated through maritime trade.
By modeling inter-and intra-continental maritime connectivity in Africa we identified countries with higher likelihood of An. stephensi introduction if facilitated through maritime movement and ranked them based on this data. Anopheles stephensi was not detected in Africa (Djibouti) until 2012. To determine whether historical maritime data would have identified the first sites of introduction, 2011 maritime data were analyzed to determine Table 2. Top 10 Countries based on LASIMTI from 2020 UNCTAD data (left). *No HSI data was available for these countries which may contribute to their drop in ranking when HSI and LASIMTI are combined in Table 3.  www.nature.com/scientificreports/ whether the sites with confirmed An. stephensi would rank highly in connectivity to An. stephensi endemic countries. Using 2011 data on maritime connectivity alone, Djibouti and Sudan were identified as the top two countries at risk of An. stephensi introduction if it is facilitated by marine cargo shipments. In 2021, these are two of the three African coastal nations where An. stephensi is confirmed to be established. When 2011 maritime data were combined with the HSI for An. stephensi establishment, the top five countries remain the same as with maritime data alone: Sudan, Djibouti, Egypt, Kenya and Tanzania, in that order. The maritime data show likelihood of introduction and HSI shows likelihood of establishment. When combined, the analyses show a likelihood of being able to establish and survive once introduced. Interestingly, the results of the combined analyses align with the detection data being reported in the Horn of Africa. The 2011 maritime data reinforces the validity of the model as it points to Sudan and Djibouti, where An. stephensi established in the following years. Similarly, the HSI data for Ethiopia has aligned closely with detections of the species to date 15 . Interestingly, around this time of initial detection in Djibouti, Djibouti City port underwent development and organizational change. The government of Djibouti took back administrative control of the port as early as 2012 30 .
Following this method, maritime trade data from 2020 could point to countries at risk of An. stephensi introduction from endemic countries as well as from the coastal African countries with newly introduced populations. Here we provide a prioritization list and heat map of countries for the early detection, rapid response, and targeted surveillance of An. stephensi in Africa based on this data and the HSI (Fig. 4). Further invasion of An. stephensi on the African continent has the potential to reverse progress made on malaria control in the last century. Anopheles stephensi thrives in urban settings and in containers, in contrast to the rural settings and   18 . Maritime data from 2020, with Djibouti and Sudan considered as potential source populations for intracontinental introduction of An. stephensi, indicate the top five countries at risk for maritime introduction are Egypt, Kenya, Mauritius, Tanzania, and Morocco, suggesting that targeted larval surveillance in these countries near seaports may provide a better understanding of whether there are maritime introductions. When the data from 2020 data is combined with HSI for An. stephensi, the top five countries are instead Egypt, Kenya, Tanzania, www.nature.com/scientificreports/ Morocco, and Libya. Interestingly, historical reports of An. stephensi in Egypt exist; however, following further identification these specimens were determined to be An. ainshamsi 31 . With several suitable habitats both along the coast and inland of Egypt, revisiting surveillance efforts there would provide insight into how countries that are highly connected to An. stephensi locations through maritime traffic may experience introductions. Further field validation of this prioritization list is necessary, because it is possible that An. stephensi is being introduced through other transportation routes, such as dry ports or airports 32 , or may even be dispersed through wind facilitation 33 . However, countries highlighted here with high levels of connectivity to known An. stephensi locations should be considered seriously at risk and surveillance urgently established to determine whether An. stephensi introduction has already occurred or to enable early detection. Primary vector surveillance for both Ae. aegypti and An. stephensi are through larval surveys, and the two mosquitoes are commonly detected in the same breeding habitats. It could therefore be beneficial to coordinate with existing Aedes surveillance efforts to be able to simultaneously gather data on medically relevant Aedes vectors while seeking to determine whether An. stephensi is present. Similarly, in locations with known An. stephensi and not well established Aedes programs, coordinating surveillance efforts provides an opportunity to conduct malaria and arboviral surveillance by container breeding mosquitoes simultaneously.
Efforts to map pinch points or key points of introduction based on the movement of goods and populations could provide high specificity for targeted surveillance and control efforts. For example, participatory mapping or population mobility data collection methods, such as those used to determine routes of human movement for malaria elimination, may simultaneously provide information on where targeted An. stephensi surveillance efforts should focus. Several methods have been proposed in the literature for modeling human movement and one in particular, PopCAB, which is often used for communicable diseases, combined quantitative and qualitative data with geospatial information to identify points of control 34 .
Data on invasive mosquito species has shown that introduction events are rarely a one-time occurrence. Population genetics data on Aedes species indicate that reintroductions are very common and can facilitate the movement of genes between geographically distinct populations, raising the potential for introduction of insecticide resistance, thermotolerance, and other phenotypic and even behavioral traits which may be facilitated by gene flow and introgression 35 . Djibouti, Sudan, Somalia, and Ethiopia, countries with established invasive populations of An. stephensi, should continue to monitor invasive populations and points of introduction to control and limit further expansion and adaptation of An. stephensi. Work by Carter et al. has shown that An. stephensi populations in Ethiopia in the north and central regions can be differentiated genetically, potentially indicating that these populations are a result of more than one introduction into Ethiopia from South Asia, further emphasizing the potential role of anthropogenic movement on the introduction of the species 17 .
One major limitation of this work is that Somalia is the third coastal nation where An. stephensi has been confirmed; however, marine traffic data were not available for Somalia so it could not be included in this analysis. The potential impact of Somalia on maritime trade is unknown and it should not be excluded as a potential source population. Additionally, this model does not account for the possibility of other countries with An. stephensi populations that have not been detected yet. As new data on An. stephensi expansion becomes available, more countries will be at higher risk. Other countries with An. stephensi populations, such as Iran, Myanmar, and Iraq, constitute lower relative percentages of trade with these countries so were not included in the analysis. However, genetic similarities were noted from An. stephensi in Pakistan, so this nation was included 10 .
Due to the nature of maritime traffic, inland countries were also not included in this prioritization ranking. Countries which are inland but share borders with high-risk countries according to the LASTIMI index should also be considered with high priority. For example, the ranking from 2011 highlights Sudan and Djibouti, both which border Ethiopia, and efforts to examine key land transportation routes between bordering nations where humans and goods travel may provide additional insight into the expansion routes of this invasive species.
In Ethiopia, An. stephensi was detected in 2016. It has largely been detected along major transportation routes although further data is needed to understand the association between movement and An. stephensi introductions and expansion since most sampling sites have also been located along transport routes. Importantly, Ethiopia relies heavily on the ports of Djibouti and Somalia for maritime imports and exports. Surveillance efforts have revealed that the species is also frequently associated with livestock shelters and An. stephensi are frequently found with livestock bloodmeals 15 . Interestingly, the original detection of An. stephensi was found in a livestock quarantine station in the port of Djibouti. Additionally, livestock constitutes one of the largest exports of maritime trade from this region. For countries with high maritime connectivity to An. stephensi locations, surveillance efforts near seaports, in particular those with livestock trade, may be targeted locations for countries without confirmed An. stephensi to begin larval surveillance.
As Ae. aegypti and Culex coronator were detected in tires or Ae. albopictus through tire and bamboo (Dracaena sanderiana) trade, An. stephensi could be carried through maritime trade of a specific good [36][37][38] . Future examination of the movement of specific goods would be beneficial in interpreting potential An. stephensi invasion pathways. Additionally, the various types of vessels used to transport certain cargo such as container, bulk, and livestock ships could affect An. stephensi survivability during transit. Sugar and grain are often shipped in bulk or break bulk vessels which store cargo in large unpackaged containers. Container ships transport products stored in containers sized for land transportation via trucks and carry goods such as tires. Livestock vessels are often multilevel, open-air ships which require more hands working on deck and water management 39 .
Using LSBCI index data from 2020, we developed a network to highlight how coastal African nations are connected through maritime trade (Fig. 4). The role of this network analysis is two-fold, (1) it demonstrates an understanding of intracontinental maritime connectivity; and (2)  www.nature.com/scientificreports/ and Kenya). This can be used as an actionable prioritization list for surveillance if An. stephensi is detected in any given country and highlights major maritime hubs in Africa which could be targeted for surveillance and control. For example, since the development of this model, An. stephensi has been detected in Nigeria. Through the use of this interactive model, Ghana, Cote d'Ivoire, and Benin have been identified as countries most connected to Nigeria through maritime trade and therefore surveillance prioritization activities could consider these locations. The network analysis reveals the significance of South African trade to the rest of the continent. Due to the distance, South Africa did not appear to be high in risk of An. stephensi introduction. However, this analysis does reveal that if An. stephensi were to enter nearby countries, it could very easily be introduced because of its high centrality. Western African countries such as Ghana, Togo, and Morocco are also heavily connected to other parts of Africa. Interestingly, Mauritius appears to be highly significant to this network of African maritime trade. Based on 2020 maritime data, Mauritius is ranked as the country with the third greatest likelihood of introduction of An. stephensi and has the second highest centrality rank value of 0.159. Considering these factors, Mauritius could serve as an important port of call connecting larger ports throughout Africa or other continents. With long standing regular larval surveillance efforts across the island for Aedes spp., this island nation is well suited to look for Anopheles larvae as part of Aedes surveillance efforts for early detection and rapid response to prevent the establishment of An. stephensi. If An. stephensi were to become established in countries with high centrality ranks, further expansion on the continent could be accelerated drastically. These ports could serve as important watchpoints and indicators of An. stephensi's incursion into Africa. Anopheles stephensi is often found in shared habitats with Aedes spp. and a great opportunity exists to leverage Aedes arboviral surveillance efforts to initiate the search for An. stephensi, especially in countries that have high potential of introduction through maritime trade.

Conclusions
With increases in globalization and volume and frequency of marine cargo traffic connecting countries and continents, information on maritime connectivity can serve as an early warning system for invasive species in general, including those relevant to public health. We show that maritime data prior to the detection of An. stephensi in Africa identified Djibouti and Sudan as countries at greatest risk of introduction, and these are locations where invasive An. stephensi populations are now established. Using data from 2020 we present a prioritization list of countries at risk of An. stephensi introduction through maritime traffic and describe intracontinental maritime connectivity. These data highlight the potential use of maritime trade data for the early detection and rapid response of invasive mosquito vectors, such as An. stephensi in Africa, to limit establishment and impact on public health.
The detection of An. stephensi in Nigeria, a country with the highest morbidity and mortality due to malaria in Africa and a major urban seaport hub, is concerning 40 . This study emphasizes the importance of leveraging Aedes surveillance efforts, conducting surveillance in ports for early detection of the species, and ensuring the predictive risk models, such as our network model here can be iterative any adaptive to include new detections as they arise.
Through integrated vector management, existing Aedes programs could be leveraged by providing training for An. stephensi identification 41 . Similarly, in locations where An. stephensi surveillance is ongoing, the addition of data collection on Aedes. spp. should be included for arboviral disease surveillance. These integrated efforts will strengthen local, regional, and national entomological surveillance systems for vector borne diseases.

Data availability
All data generated are included in this manuscript and supplementary files.